DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 10 and 19 recite limitation "said master ingestion and data automation system". There is insufficient antecedent basis for this limitation in the respective claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea as a mental process without significantly more.
Claims 1, 10 and 19 are rejected under 35 U.S.C. 101.
Step 1
Claim 1 recites “A master ingestion and data automation system” including a processor and a memory (spec. pp. 17). Claim 10 recites “A method”. Claim 19 recites “A non-transitory computer readable storage media” containing executable computer program instructions. Each is directed to a statutory category.
Step 2A – Prong One
Claims 1, 9 and 17 recite the following limitations directed to an abstract idea:
“standardizes and normalizes data using source-specific rules” recites an abstract idea as a mental process. One can mentally observe or evaluate by using rules to standardize and normalize source data.
“collecting and/or creating…cluster of legal entities, names and/or addresses” and “collecting data and capturing insights” recite an abstract idea as a mental process. One can mentally evaluate and judge to extract metadata from source data, based on which to cluster source data, in order to derive insights.
“cluster data based on rules” recites an abstract idea as a mental process. One can mentally evaluate and judge by using rules to cluster source data based on extracted metadata.
“determine which data to publish” recites an abstract idea as a mental process. One can mentally evaluate and judge to determine which data can be published.
Step 2A – Prong Two
This judicial exception is not integrated into a practical application.
Claims 1, 10 and 19 include additional elements “meta data driven framework (MDD)” and “executable computer program instructions”, which are high-level recitations of generic computer components and functions that represent mere instructions to apply on a computer per MPEP §2106.05(f).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Step 2B
Claims 1, 10 and 19 include additional elements “receives incoming files”, “receives said incoming files to poll and recognize new files”, “receives all the data from said staging tables and/or said cluster data” and “publishes output decisions”, which qualify as “i. Receiving or transmitting data over a network”, and is recognized by the courts as well-understood, routine, and conventional per MPEP 2106.05(d)(II).
Claims 1, 10 and 19 include additional element “loads said recognized files into staging tables”, which qualifies as “iv. Storing and retrieving information in memory”, and is recognized by the courts as well-understood, routine, and conventional activity per MPEP §2106.05(d)(II).
The conclusions on the mere instructions to apply the abstract idea using generic computer components and functions carry over and do not add significantly more or provide any "inventive concept".
In summary, independent claims 1, 10 and 19 are not eligible. Claims 2-9 and 11-18 depend on claims 1 and 10 respectively and recite the same abstract idea.
Step 2A Prong One
The following claims recite additional elements that are mentally performable.
Claims 2 and 11 recite that incoming file are batch files, which is an abstract idea as a mental process. One can mentally observe and evaluate to determine if incoming files are batch files.
Claims 3 and 12 recite representing ingested and cluster data by entries in an ingestion journal, which is an abstract idea as a mental process. One can mentally observe and evaluate to capture properties of ingested and cluster data in journal entries.
Claims 5 and 14 recite various services included in the collection services engine, which is an abstract idea as a mental process. One can mentally observe and evaluate to perform a group of common services.
Claims 7 and 16 recite updating cluster data based on changes in clustering properties, which is an abstract idea as a mental process. One can mentally observe and evaluate to update those clusters impacted by changes in clustering properties.
Claims 9 and 18 recite publishing processed data after various steps, each of which is an abstract idea as a mental process. One can mentally evaluate and judge to determine whether the processed data is ready to publish.
Step 2A Prong Two
Claims 4 and 13 recite additional elements “GKE cluster” and “python API”, which are high-level recitations of generic computer components and functions that represent mere instructions to apply on a computer.
Step 2B
Claims 6 and 15 recite storing processed data in staging tables in a repository, which qualifies as “iv. Storing and retrieving information in memory”, and is recognized by the courts as well-understood, routine, and conventional.
Claims 8 and 17 recite receiving data from various data services, which qualifies as “i. Receiving or transmitting data over a network”, and is recognized by the courts as well-understood, routine, and conventional.
In summary, these dependent claims do not add any additional elements sufficient to make the claims non-abstract. Therefore, they are not eligible accordingly.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5-6, 10-12, 14-15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Dhanakodi et al. US patent 11,294,926 [herein “Dhanakodi”], and further in view of Langseth et al. US patent application 2007/0011175 [herein “Langseth”].
Claim 1 recites “A master ingestion and data automation system which comprises: a. a source ingestion module which receives incoming files of meta data driven framework (MDD) which standardizes and normalizes data using source-specific rules; b. a recognizer engine which receives said incoming files to poll and recognize new files into said master ingestion and data automation system as recognized files; c. a loader which loads said recognized files into staging tables;”
Dhanakodi receives data (i.e., incoming files) ingested from multiple data sources with disparate proprietary data formats and input types (fig. 1, #42) and prepares data into clean (i.e., normalized) standard data output, where each source is configured with tailored instructions (i.e., rules) to parse, transform, refine and supplement input data (3:66-4:5). Output data (i.e., recognized files) is loaded to tables (i.e., staging tables) of a cloud database (5:56-60).
Dhanakodi does not disclose limitations “d. a collection services engine, wherein said collection services engine processes said recognized files by: i. collecting and/or creating at least one cluster of legal entities, names, and/or addresses; ii. collecting data and capturing insights other than said legal entities, names, and/or addresses; and iii. cluster data based on rules without storing said data redundantly;”
However, Langseth teaches a middleware software system (i.e., MDD) of entity extraction tools to search unstructured text documents for (i.e., collecting) terms identifying specific types of entities, such as people (i.e., names), places (i.e., addresses), organizations (i.e., legal entities) (Langseth: [0005]). Extracted entities are grouped (i.e., clustered) by hierarchies (Langseth: [0019]), including entity hierarchy based on an ontology (Langseth: [0110]) and relationship hierarchy (i.e., metadata), where analysis across hierarchies can provide (i.e., capture) interesting insights into the events and topics discussed in a corpus of documents (Langseth: [0113]). Langseth converts transformation output into a common consistent format, while retaining metadata and links (i.e., without duplication) back to the original source data (Langseth: [0045]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Langseth to Dhanakodi. One having ordinary skill in the art would have found motivation to incorporate the entity extraction tool of Langseth in the ETL application of Dhanakodi to generate structured data from unstructured data sources.
Claim 1 further recites “e. an evaluation and decisioning module that retrieves all of the data from said staging tables and/or said cluster data to determine which data to publish; and f. a publisher module which publishes output decisions from said evaluation and decisioning module.”
Before data is ready for transmission to the cloud database, Dhanakodi performs transformation, anonymization (i.e., condition to publish), synthetic data operation, tagging and compression (5:33-36). Result data is loaded (i.e., published) to the cloud database (5:56-60).
Claims 10 and 19 are analogous to claim 1, and are similarly rejected.
Claim 2 recites “The system according to claim 1, wherein said incoming files are batch files.”
In Dhanakodi, common file settings include batch size, indicating for example that 100K lines of raw data (i.e., incoming files) may be read from a data source at a time (7:8-15).
Claim 11 is analogous to claim 2, and is similarly rejected.
Claim 3 recites “The system according to claim 1, wherein said data from said staging tables and/or cluster data are identified by entries from an ingestion journal together with any related data comprising source domain and precedence.”
In Dhanakodi, ETL jobs can be configured by global settings, such as where events (e.g., ingestion) are logged (i.e., journaled), as well as source-specific custom settings, such as a propriety format used for data from a specific source (6:41-44).
Claim 12 is analogous to claim 3, and is similarly rejected.
Claim 5 recites “The system according to claim 1, wherein said collection services engine comprises at least one collection service selected from the group consisting of: source organization collection service, name collection service, address collection service, phone collection service, industry codes/line of business collecting service, role player identification collection service, contact title collection service, contact details collection service, start year collection service, employee collection service, URL/email collection service, and combinations thereof.”
Dhanakodi and Langseth teach claim 1, where Langseth uses entity extraction tools to search unstructured text documents for (i.e., collecting) terms identifying specific types of entities, such as people (i.e., names), places (i.e., addresses), organizations (i.e., legal entities) (Langseth: [0005]). Extracted entities are grouped (i.e., clustered) by hierarchies (Langseth: [0019]), including entity hierarchy based on an ontology (Langseth: [0110]) and relationship hierarchy, where analysis across hierarchies can provide (i.e., capture) interesting insights into the events and topics discussed in a corpus of documents (Langseth: [0113]).
Claim 14 is analogous to claim 5, and is similarly rejected.
Claim 6 recites “The system according to claim 5, wherein said data from said staging tables and/or said cluster data are stored in a collection service repository after being processed in said at least one collection service.”
Dhanakodi teaches claim 5, but does not disclose this claim; however, the capture schema of Langseth serves as a repository for data captured by the extraction connectors and to hold (i.e., store) results of the transformation connectors (Langseth: [0070]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Langseth to Dhanakodi. One having ordinary skill in the art would have found motivation to incorporate the repository of captured data of Langseth in the ETL application of Dhanakodi to generate structured data from unstructured data sources.
Claim 15 is analogous to claim 6, and is similarly rejected.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Dhanakodi as applied to claims 1 and 10 respectively, in view of Langseth, and further in view of Develop and Deploy a Python API with Kubernetes and Docker -- part II. June 2023, pp. 1-19. https://medium.com/@MetricFire/develop-and-deploy-a-python-api-with-kubernetes-and-docker-part-ii-a31d9c61817 [herein “MetricFire”].
Claim 4 recites “The method according to claim 1, wherein said collection services engine comprises at least one GKE cluster containing a python API.”
Dhanakodi and Langseth teach claim 1, but do not disclose this claim; however, MetricFire develops an application with Python API and deploys it to a GKE cluster (MetricFire: pp. 2/19).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of MetricFire to Dhanakodi and Langseth. One having ordinary skill in the art would have found motivation to develop/deploy the ETL application of Dhanakodi with the entity extraction tools of Langseth using Python API and to a GKE cluster of MetricFire.
Claim 13 is analogous to claim 4, and is similarly rejected.
Claims 7-9 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Dhanakodi as applied to claims 1 and 10 respectively, in view of Langseth, and further in view of Joerg et al. US patent application 2011/0055147 [herein “Joerg”].
Claim 7 recites “The system according to claim 6, wherein said cluster data is updates since the last decision for at least one cluster selected from the group consisting of: name clusters, address clusters, industry codes/line of business identification, contact details clusters, contact title collection, role player identification, employee clusters, and combinations thereof.”
Dhanakodi and Langseth teach claim 6, but do not disclose this claim; however, an ETL job in Joerg performs exhaustive operations for extracting, transforming, and loading data from source systems to a target system (Joerg: [0006]). An incremental ETL job is instantiated to perform incremental extract, transform, and load of changed (i.e., updated since last load) data from the sources (Joerg: [0044]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Joerg to Dhanakodi and Langseth. One having ordinary skill in the art would have found motivation to incorporate the incremental ETL job of Joerg in the ETL application of Dhanakodi and Langseth to avoid processing of unchanged data.
Claim 16 is analogous to claim 7, and is similarly rejected.
Claim 8 recites “The system according to claim 5, wherein said evaluation and decisioning module receives data from at least one data source selected from the group consisting of: said at least one collection service, source and domain precedence, Duns insight, metadata insight, cluster changes, and combinations thereof.”
Dhanakodi and Langseth teach claim 5, but do not disclose this claim; however, Joerg instantiates an incremental ETL job to perform incremental extract, transform, and load of changed data (i.e., cluster changes) (Joerg: [0044]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Joerg to Dhanakodi and Langseth. One having ordinary skill in the art would have found motivation to incorporate the incremental ETL job of Joerg in the ETL application of Dhanakodi and Langseth to avoid processing of unchanged data.
Claim 17 is analogous to claim 8, and is similarly rejected.
Claim 9 recites “The system according to claim 1, wherein said data to publish is at least one selected from the group consisting of: file build, update (change/fill), confirm, wait, potential linkage, special handling (high risk), and additional match points.”
Dhanakodi and Langseth teach claim 1, but do not disclose this claim; however, Joerg instantiates an incremental ETL job to perform incremental extract, transform, and load of changed data (i.e., updates) (Joerg: [0044]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Joerg to Dhanakodi and Langseth. One having ordinary skill in the art would have found motivation to incorporate the incremental ETL job of Joerg in the ETL application of Dhanakodi and Langseth to avoid processing of unchanged data.
Claim 18 is analogous to claim 9, and is similarly rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. For example, Pellegrini et al. US patent application 2009/0177671.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHELLY X. QIAN whose telephone number is (408)918-7599. The examiner can normally be reached Monday - Friday 8-5 PT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at (571)270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHELLY X QIAN/Examiner, Art Unit 2154
/SYED H HASAN/Primary Examiner, Art Unit 2154